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Robust AFR estimation using the ion current and neural networks
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0002-4143-2948
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1999 (English)In: SAE transactions, ISSN 0096-736X, Vol. 108, no 03, p. 1585-1589Article in journal (Refereed) Published
Abstract [en]

A robust air/fuel ratio "soft sensor" is presented based on non-linear signal processing of the ion current signal using neural networks. Care is taken to make the system insensitive to amplitude variations, due to e.g. fuel additives, by suitable preprocessing of the signal. The algorithm estimates the air/fuel ratio to within 1.2% from the correct value, defined by a universal exhaust gas oxygen (UEGO) sensor, when tested on steady state test-bench data and using the raw ion current signal. Normalizing the ion current increases robustness but also increases the error by a factor of two. The neural network soft sensor is about 20 times better in the case where the ion current is not normalized, compared with a linear model. On normalized ion currents the neural network model is about 4 times better than the corresponding linear model. Copyright © 1999 Society of Automotive Engineers, Inc.

Place, publisher, year, edition, pages
New York: Society of Automotive Engineers, 1999. Vol. 108, no 03, p. 1585-1589
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-5557DOI: 10.4271/1999-01-1161Scopus ID: 2-s2.0-84877183227OAI: oai:DiVA.org:hh-5557DiVA, id: diva2:408957
Available from: 2011-04-06 Created: 2010-09-02 Last updated: 2018-03-23Bibliographically approved
In thesis
1. Virtual sensing of combustion quality in SI engines using the ion current
Open this publication in new window or tab >>Virtual sensing of combustion quality in SI engines using the ion current
2004 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Göteborg: Chalmers tekniska högskola, 2004. p. 70
Series
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie, ISSN 0346-718x ; 2207
Keywords
Internal Combustion Engines, Spark Ignition, Estimation, Control, Neural Networks, Ion Current
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:hh:diva-667 (URN)2082/1009 (Local ID)91-7291-525-0 (ISBN)2082/1009 (Archive number)2082/1009 (OAI)
Public defence
2004-11-19, Wgforssalen, Halmstad, 10:15 (English)
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Supervisors
Available from: 2007-05-07 Created: 2007-05-07 Last updated: 2016-04-13Bibliographically approved

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Munther, ThomasRögnvaldsson, ThorsteinnWickström, Nicholas

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